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Robustification is a form of optimisation whereby a system is made less sensitive to the effects of random variability, or noise, that is present in that system's input variables and parameters. The process is typically associated with engineering systems , but the process can also be applied to a political policy , a business strategy or any ...
Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought against uncertainty that can be represented as deterministic variability in the value of the parameters of the problem itself and/or its solution.
For any two 2 (m1+m2 )-(p1+p2) fractional factorial robust parameter designs, D1 and D2, we say that D1 has less aberration than D2 if there exists an r such that, B i (D1) = B i (D2) for all i < r – 1 and B r (D1) < B r (D2). If no other design has less aberration than D1, then D1 is the minimum aberration fractional factorial robust ...
In the same line robustness can be defined as "the ability of a system to resist change without adapting its initial stable configuration". [1] "Robustness in the small" refers to situations wherein perturbations are small in magnitude, which considers that the "small" magnitude hypothesis can be difficult to verify because "small" or "large ...
H-infinity loop-shaping is a design methodology in modern control theory.It combines the traditional intuition of classical control methods, such as Bode's sensitivity integral, with H-infinity optimization techniques to achieve controllers whose stability and performance properties hold despite bounded differences between the nominal plant assumed in design and the true plant encountered in ...
Taguchi methods (Japanese: タグチメソッド) are statistical methods, sometimes called robust design methods, developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to engineering, [1] biotechnology, [2] [3] marketing and advertising. [4]
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Bayes linear statistics is a subjectivist statistical methodology and framework. Traditional subjective Bayesian analysis is based upon fully specified probability distributions, which are very difficult to specify at the necessary level of detail.